Conventionally, to perform data forecasting, users typically build a forecasting model from input data directly at the time interval level that is specified for the forecasting. For example, if a user desires to make a monthly forecast of sales, then monthly sales data is used directly to build a model; the model is then used in performing the forecast.
At the specified forecasting level (e.g., monthly forecasting level), historical data may exhibit irregular or unexplainable volatility, which may result in a derived forecasting model that may be incapable of capturing some of the key and hidden drivers that may be visible at a different time interval level (e.g., at a quarterly level). Consequently, users may develop forecasting models that produce inaccurate forecast data.